Introduction: This analysis is based on the outputs of pairwise comparisons of differential gene expression generated by this template. It uses results from 3 pairwise comparisons of 3 sample groups vs. their corresponding control groups and compares how these 3 sample groups are different from each other in terms of their sample-control differences (delta-delta). An example of such analysis is the different responses of 3 cell types to the treatment of the same drug. This analysis is focused on the overlapping of differentially expression at both gene and gene set levels.
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Transcriptome in immune cells of control-patient samples
Rna-seq data was generated from of 2 types of immune cells of 3 controls and 3 patients. Raw data was processed to get gene-level read counts. Pairwise comparisons were performed between controls and patients in each immune cell.
This is a demo.
This report compares the results of the following pairwise comparisons.
Both comparisons reported the log ratio of 2 group means for each gene. The global agreement of log ratios of all genes indicates how much the results of these 2 comparisons are similar to or different from each other. Full table of gene-level statistics side-by-side is here.
Both comparisons identified DEGs between 2 compared groups. Overlapped DEGs identified by both comparisons are worthy of a closer look.
| B_Cell | T_Cell | |
|---|---|---|
| Higher in the 2nd group | 984 | 693 |
| Lower in the 2nd group | 1853 | 671 |
Figure 2. Overlapping of DEGs. All combinations of differential expression towards opposite directions are plotted and Fisher’s exact test is performed to evaluate the significance of overlapping or lack of overlapping. Click links below to view overlapping DEGs:
2-way ANOVA analysis is performed to identify genes responding to SLE differently in different Cell. The analysis reported 3 p values, corresponding to the effect of SLE, Cell, and their interaction. The analysis identified 1127 significant genes with interaction p values less than 0.01. The ANOVA results are summarized in a table here.
Genes are often grouped into pre-defined gene sets according to their function, interaction, location, etc. Analysis then can be performed on genes in the same gene set as a unit instead of individual genes.
Average differential expression of genes in the same gene set. The gene set-level statistics were fully summarized in this table here.
Each 2-group comparison performs gene set over-representation analysis (ORA) that identifies gene sets over-represented with differentially expressed genes. The results of ORA of both 2-group comparisons are summarized and compared here. The ORA of each gene set reports an odds ratio and p value. These statistics from both comparisons were combined and listed side-by-side, as well as the difference of their odds ratios and ratio of their p values (p set to 0.5 when not available), in this table here
| B_Cell::Higher_in_Control | B_Cell::Higher_in_SLE | T_Cell::Higher_in_Control | T_Cell::Higher_in_SLE | |
|---|---|---|---|---|
| BioSystems | 438 | 3212 | 438 | 3212 |
| KEGG | 40 | 319 | 40 | 319 |
| MSigDb | 857 | 4125 | 857 | 4125 |
| OMIM | 0 | 1 | 0 | 1 |
| PubTator | 123 | 7634 | 123 | 7634 |
Figure 5. The overlapping of over-represented gene sets from both comparisons. Click links below to view tables of overlapping significant gene sets:
Each 2-group comparison performs gene set enrichment analysis (GSEA) on genes ranked by their differential expression. The results of GSEA of both 2-group comparisons are summarized and compared here. The GSEA of each gene set reports an enrichment score and p value. These statistics from both comparisons were combined and listed side-by-side in this table here
| B_Cell::Higher_in_Control | B_Cell::Higher_in_SLE | T_Cell::Higher_in_Control | T_Cell::Higher_in_SLE | |
|---|---|---|---|---|
| C0_Hallmark | 2 | 37 | 2 | 37 |
| C1_Positional | 13 | 26 | 13 | 26 |
| C2_BioCarta_Pathways | 1 | 68 | 1 | 68 |
| C2_Chemical_and_genetic_perturbations | 36 | 1356 | 36 | 1356 |
| C3_MicroRNA_targets | 0 | 51 | 0 | 51 |
| C3_TF_targets | 4 | 284 | 4 | 284 |
| C4_Cancer_gene_neighborhoods | 42 | 86 | 42 | 86 |
| C4_Cancer_modules | 10 | 176 | 10 | 176 |
| C6_Oncogenic_signatures | 2 | 116 | 2 | 116 |
| C7_Immunologic_signatures | 58 | 922 | 58 | 922 |
| GO_BP | 145 | 2065 | 145 | 2065 |
| GO_CC | 67 | 159 | 67 | 159 |
| GO_MF | 44 | 359 | 44 | 359 |
| KEGG_compound | 4 | 126 | 4 | 126 |
| KEGG_enzyme | 1 | 1 | 1 | 1 |
| KEGG_module | 11 | 13 | 11 | 13 |
| KEGG_pathway | 9 | 161 | 9 | 161 |
| KEGG_reaction | 2 | 35 | 2 | 35 |
| OMIM_gene | 1 | 2 | 1 | 2 |
| REACTOME | 92 | 283 | 92 | 283 |
| WikiPathways | 2 | 91 | 2 | 91 |
Figure 7. The overlapping of over-represented gene sets from both comparisons. Click links to view tables of overlapping significant gene sets from GSEA:
The top 1000 genes with significant ANOVA p values (p <= ‘r prms\(geneset\)cluster$panova’) were used as seeds to perform a gene-gene clustering analysis and 5 clusters were identified. ORA was performed on the clusters to identify their functional association (see table below);
| ID | Size | B_Cell::Control | B_Cell::SLE | T_Cell::Control | T_Cell::SLE | Gene_set |
|---|---|---|---|---|---|---|
| Cluster_1 | 895 | 0 | -1.6039 | 0 | -1.5890 | 4328 |
| Cluster_2 | 781 | 0 | 1.3818 | 0 | -1.3717 | 830 |
| Cluster_3 | 1000 | 0 | 1.6673 | 0 | 1.5826 | 2324 |
| Cluster_4 | 81 | 0 | 0.2312 | 0 | 1.5631 | 1290 |
| Cluster_5 | 396 | 0 | -1.4606 | 0 | 1.3153 | 3410 |
Check out the RoCA home page for more information.
To reproduce this report:
Find the data analysis template you want to use and an example of its pairing YAML file here and download the YAML example to your working directory
To generate a new report using your own input data and parameter, edit the following items in the YAML file:
Run the code below within R Console or RStudio, preferablly with a new R session:
if (!require(devtools)) { install.packages('devtools'); require(devtools); }
if (!require(RCurl)) { install.packages('RCurl'); require(RCurl); }
if (!require(RoCA)) { install_github('zhezhangsh/RoCAR'); require(RoCA); }
CreateReport(filename.yaml); # filename.yaml is the YAML file you just downloaded and edited for your analysis
If there is no complaint, go to the output folder and open the index.html file to view report.
## R version 3.2.2 (2015-08-14)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.5 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] DEGandMore_0.0.0.9000 snow_0.4-1 rchive_0.0.0.9000
## [4] gplots_3.0.1 MASS_7.3-45 htmlwidgets_0.6
## [7] DT_0.1 awsomics_0.0.0.9000 yaml_2.1.13
## [10] rmarkdown_0.9.6 knitr_1.13 RoCA_0.0.0.9000
## [13] RCurl_1.95-4.8 bitops_1.0-6 devtools_1.12.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.5 magrittr_1.5 highr_0.6
## [4] stringr_1.0.0 caTools_1.17.1 tools_3.2.2
## [7] parallel_3.2.2 KernSmooth_2.23-15 withr_1.0.2
## [10] htmltools_0.3.5 gtools_3.5.0 digest_0.6.9
## [13] formatR_1.4 memoise_1.0.0 evaluate_0.9
## [16] gdata_2.17.0 stringi_1.1.1 jsonlite_0.9.22
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